Plenary Talks

Prof. Masayoshi Tomizuka

Prof. Masayoshi Tomizuka

University of California, Berkeley

Mechatronics in the year 2022 in the UCB/MSC Laboratory

Abstract: I presented a talk, “Mechatronics: From the 20th to 21st Century,” at the first IFAC Conference on Mechatronic Systems in 2000, Darmstadt. In the talk, I gave the Y2K definition of mechatronics: The synergetic integration of physical systems with electronics/information technology and complex-decision making (in the design, manufacture and operation of industrial products and processes). This definition is still appropriate and captures the essence of mechatronics. I should note, however, that important new tools have been added to the toolboxes of electronics/information technology and complex-decision making. In terms of complex decision making AI/Machine Learning has gained popularity. The toolbox for electronics/information technology include IoT, Virtual Reality(VR) and Augmented Reality(AR). On the other hand, mechatronics is now an established discipline as popular as controls, signal processing, sensors, and so on, There are millions of websites on mechatronics and tons of textbooks. Thus, my talk will focus on what is going on around me and I will introduce recent works in my research group. Yet, they are good examples of trendy mechatronics. More specifically, examples are taken from research in the areas of autonomous driving and intelligent robots.

Bio: Dr. Masayoshi Tomizuka received his B.S. and M.S. degrees in Mechanical Engineering from Keio University, Tokyo, Japan and his Ph. D. degree in Mechanical Engineering from the Massachusetts Institute of Technology in February 1974. In 1974, he joined the faculty of the Department of Mechanical Engineering at the University of California at Berkeley, where he currently holds the Cheryl and John Neerhout, Jr., Distinguished Professorship Chair and serves as Associate Dean for the Faculty in the College of Engineering. He served as Director and CEO of Berkeley Education Alliance for Research in Singapore (BEARS) in Singapore from 2014 to 2016. He teaches courses in dynamic systems and controls. His current research interests are optimal and adaptive control, digital control, motion control, and control problems related to robotics and manufacturing, vehicles and mechatronic systems. He served as Program Director of the Dynamic Systems and Control Program of the National Science Foundation (2002-2004). He has supervised about 130 Ph. D. students to completion. He has published over 900 articles in professional journals and conference proceedings. He served as President of the American Automatic Control Council (AACC) (1998-99). He chaired the Publication Committee and Award Committee of the International Federation of Automatic Control (IFAC), and he chaired the IFAC Technical Committee on Mechatronic Systems. He is Honorary Member of the ASME, Life Fellow of the Institute of Electric and Electronics Engineers (IEEE), and Fellow of IFAC and the Society of Manufacturing Engineers (SME). He is the recipient of the J-DSMC Best Paper Award (1995, 2010), the DSCD Outstanding Investigator Award (1996), the Charles Russ Richards Memorial Award (ASME, 1997), the Rufus Oldenburger Medal (ASME, 2002), the John R. Ragazzini Award (AACC, 2006), the Richard Bellman Control Heritage Award (AACC, 2018), the Honda Medal (ASME, 2019) and the Nichols Medal (IFAC, 2020). He is a member of the United State National Academy of Engineering.

Prof. John Ringwood

Prof. John Ringwood

Maynooth University, Ireland

Energy maximizing control of wave energy systems

Abstract: Though wave energy systems are not yet commercial, control has been identified as an important enabling technology which can reduce the cost of wave energy, allowing it to compete economically with other renewable and conventional energy sources. However, wave energy systems, which are diverse in form and operating principle, represent a challenging control problem, in terms of panchromatic reciprocating energy flux, hydrodynamic modelling complexity, non-causality in the fundamental control solution, and adverse sensitivity properties. In addition, the wave energy control problem is expressed in terms of an energy maximising performance function, rather than being easily reduced to a set-point following problem, while a key system input variable, the wave excitation force, is unmeasurable. This talk will detail the major control issues faced in dealing with wave energy systems, also providing an overview of wave energy technology and some typical devices, while showing some possibilities in the solution domain. Some experimental control results will also be presented, and the talk will conclude with some perspectives on future research directions.

Bio: Dr. John Ringwood received the HonsDipEE from TU Dublin, the BSc (Eng) in electrical engineering from Trinity College Dublin (both in 1981), and the PhD in control systems from Strathclyde University (1984). He subsequently received an MA in music technology from Maynooth University in 2005. He spent 15 years in Dublin City University as a member of academic staff in the School of Electronic Engineering, with concurrent terms as a visiting academic in Massey University and the University of Auckland. He joined Maynooth University in 2000, as chair professor and founding head of the Dept. of Electronic Engineering and built the Dept. from a greenfield site, also serving as Dean of Engineering from 2001 to 2006. He is currently Professor of Electronic Engineering and Director of the Centre for Ocean Energy Research in Maynooth University. He is Associate Editor for IEEE Trans. on Sustainable Energy and the Journal of Ocean Engineering and Marine Energy, Subject Editor for Energies, and Deputy Subject Editor for IET RPG. John received the 2016 IEEE Control Systems Magazine Outstanding Paper Award and was awarded Chevalier des Palmes Academiques by the French Government in 2017 for his contribution to ocean energy research. In addition to over 400 peer-reviewed publications, he is co-author of the book Hydrodynamic Control of Wave Energy Devices (with Umesh Korde) and holds 3 patents. His commercialization activities, which include the spin-out company Wave Venture, has been recognized by Enterprise Ireland (2008 Industrial Technologies Commercialization Award) and Maynooth University (2013 Commercialisation Award). His research interests are in ocean and renewable energy, control systems, and biomedical engineering.

Prof. Kon-Well Wang

Prof. Kon-Well Wang

University of Michigan, Ann Arbor

Reconfigurable Metastructures – From Wave and Vibration Controls to Mechano-Intelligence

Abstract: In recent years, the concept of mechanical metastructures developed based on nature-inspired modular architectures has been explored to create advanced engineering systems. For example, inspired by the observation that some of skeletal muscle’s intriguing macroscale functionalities result from the assembly of nanoscale cross-bridge constituents with metastability, the idea of synthesizing structures from the integration of mechanical metastable modules has been pursued. In another example, inspired by the physics behind the plant nastic movements and the rich designs of origami folding, a class of metastructures is created building on the innovation of fluidic-origami modular elements. Overall, the metastructure modules are designed to be reconfigurable in their shape, mechanical properties, multi-stability features, and dynamic characteristics, so to produce synergistic and intriguing functionalities at the system level, such as programmable vibration isolation, phononic bandgap control and nontraditional wave steering. More recently, with the rapid advances in high-performance intelligent systems, we are witnessing a prominent demand for the next generation of metastructures to have much more built-in intelligence and autonomy. An emerging direction is therefore to pioneer and harness the metastructures’ high dimensionality, multi-stability, and nonlinearity for mechano-intelligence via physical computing. That is, we aim to concurrently embed computing power and functional intelligence, such as observation, learning, memorizing, decision-making and execution, directly in the mechanical domain, advancing from conventional systems that solely rely on add-on electronics to achieve intelligence. This presentation will highlight some of these recent advancements in reconfigurable metastructures, from adaptive wave and vibration control to self-learning-self-tuning structural intelligence.

Bio: Dr. Kon-Well Wang is the Stephen P. Timoshenko Professor of Mechanical Engineering (ME) at the University of Michigan (U-M). He has been the U-M ME Department Chair from 2008 to 2018, and has served as a Division Director at the U.S. National Science Foundation for two years in 2019-20, via an Executive Intergovernmental Personnel Act rotator appointment. Wang received his Ph.D. degree from the University of California, Berkeley, worked at the General Motors Research Labs as a Sr. Research Engineer, and started his academic career at the Pennsylvania State University in 1988. At Penn State, Wang has served as the William E. Diefenderfer Chaired Professor, co-founder and Associate Director of the Vertical Lift Research Center of Excellence, and a Group Leader of the Center for Acoustics & Vibration. He joined the U-M in 2008. Wang’s main technical interests are in structural dynamics and controls, especially in the emerging fields of intelligent structural & material systems, programmable metastructures and metamaterials, and origami mechanics & dynamics, with applications in vibration, wave & noise controls, vibration energy harvesting, and sensing & monitoring. He has received various recognitions, such as the ASME Rayleigh Lecture Award, the Pi Tau Sigma-ASME Charles Russ Richards Memorial Award, the ASME J.P. Den Hartog Award, the SPIE Smart Structures and Materials Lifetime Achievement Award, the ASME Adaptive Structures and Materials Systems Prize, the ASME N.O. Myklestad Award, and the ASME Rudolf Kalman Award. He has been Chair of the ASME Technical Committee on Vibration and Sound, Chair of the ASME ME Department Heads Executive Committee, Editor in Chief for the ASME Journal of Vibration & Acoustics, and an Associate Editor or Editorial Board Member for various journals. Wang is a Fellow of the ASME, AAAS, and IOP.

Prof. Rolf Findeisen

Prof. Rolf Findeisen

TU Darmstadt, Germany

Safe – yet optimal and flexible control and planning for mechatronic systems fusing machine learning and predictive control

Abstract: Mechatronic systems are often inherently complex, show nonlinear dynamics, and are subject to significant uncertainties and disturbances, challenging optimal yet safe operation. Fusing predictive control with machine learning approaches is promising for controlling and planning such systems in dynamic and uncertain environments. The combination allows to adapt and learn from data, fusing data-driven and model-based information while allowing individualization and personalization. We present results towards the fusion of predictive control and machine learning methods for the control and planning of mechatronic systems. Focus is put on integrating systems knowledge and properties, such as controllability and constraint satisfaction, into learning to achieve safe and stable yet optimal behavior. The presented methods are underlined, considering the control and planning of mechatronic systems spanning from robotics, drones, and autonomous driving to quantum dot microscopy.

Bio: Rolf Findeisen received a Diploma in Engineering Cybernetics from the University in Stuttgart in Engineering, an MSc from the University of Wisconsin–Madison, and the Dr.-Ing. from the University of Stuttgart. He had research stays at ETH Zürich, the Massachusetts Institute of Technology Cambridge, EPF Lausanne, the University of California at Santa Barbara, Imperial College London, NTNU Trondheim, Norway. After heading the Systems Theory and Automatic Control Laboratory, Otto-von-Guericke University Magdeburg, Germany, he leads the Control and Cyber-physical systems laboratory at TU Darmstadt. Rolf has been/is editor and associate editor of various journals, e.g., IEEE Control Systems Magazine , IEEE Transactions on Networked Systems, J. Optimal Control Applications, and Methods and Processes . He was the IPC Chair of the IFAC World Congress 2020. The research interests of his group include the control of autonomous systems, predictive control, fusing learning and control, cyber-physical systems, uncertainty, and robustness. Applications span from mechatronics, robotics, and autonomous driving, to biotechnology, batteries, energy systems, and systems medicine.

Prof. Keqiang Li

Prof. Keqiang Li

Tsinghua University, China

Integrated Decision and Control for Self-evolving Autonomous Vehicles

Abstract: Today’s self-driving system faces increasing challenges from highly complex, random and dense traffic scenarios in city road conditions. Existing hierarchical design method, for example, that with rule-based decision and linear motion controller, is very lack of adaptability. As a biologically inspired artificial intelligence, reinforcement learning (RL) is promising to provide a kind of self-evolving ability for an automated car, which has the potential to generalize to unknown driving scenarios. This talk will discuss recent advances in designing self-evolving autonomous driving systems for high-level driving intelligence. An interpretable and computationally efficient integrated framework has been proposed to realize more flexible decision and control functionality in autonomous driving, in which the standard reinforcement learning architecture, i.e., actor-critic, can be subtly utilized to train its static path planner and dynamic optimal tracker. Some challenges and future trends are also discussed to inspire more creative designs in this field.

Bio: Dr. Keqiang Li is currently a professor at School of Vehicle and Mobility, Tsinghua University. He is the Academician of Chinese Academy of Engineering. He also serves as the director of State Key Laboratory of Automotive Safety and Energy, and chief scientist of National Innovation Center of Intelligent and Connected Vehicles.
Dr. Li is an expert in the field of automotive intelligence. His main research areas include dynamic design and intelligent control of driver assistance systems and autonomous driving systems. He has authored about 200 journal/conference papers and over 80 patents in and outside of China. He has worked in Japanese and Germany automotive companies and academic institutions for many years including Tokyo University of Agriculture and Technology, The University of Tokyo, Aachen University of technology, National Traffic Safety & Environment Lab in Japan, Isuzu Automobile Corp, etc. Dr. Li is also the recipient of Changjiang Scholar Program Professor, China National Technological Invention Award, and China National Scientific and Technological Progress Award.